Indexing in SQL Server Database Engine
Indexing is a fundamental database concept that significantly impacts query performance. This document provides a comprehensive overview of indexing within the SQL Server Database Engine.
Introduction to Indexing
An index is a data structure that improves the speed of data retrieval operations on a database table. It works similarly to an index in a book, allowing the database engine to quickly locate specific rows without scanning the entire table.
Indexes are created on one or more columns of a table or view. When a query is executed, the SQL Server query optimizer can use an index to find the requested data more efficiently, especially in large tables.
Types of Indexes
SQL Server supports several types of indexes, each with its own characteristics and use cases:
Clustered Indexes
A clustered index defines the physical order of data in a table. Because the data rows themselves are stored in the leaf nodes of the index, a table can have only one clustered index.
- The table data is sorted and stored based on the clustered index key.
- The leaf level of a clustered index is the data.
- Ideal for columns that are frequently searched for ranges of values (e.g., dates, IDs).
Nonclustered Indexes
A nonclustered index contains the index key values and a pointer to the data row. The physical order of the data rows is not affected by a nonclustered index. A table can have multiple nonclustered indexes.
- A nonclustered index is a separate structure from the data.
- The leaf nodes contain index key values and row locators (e.g., Row ID or clustered index key).
- Useful for columns that are frequently used in WHERE clauses or JOIN conditions, but not the primary sorting column.
Unique Indexes
A unique index enforces the uniqueness of values in one or more columns. It prevents duplicate values from being inserted into the indexed columns. Both clustered and nonclustered indexes can be unique.
Filtered Indexes
A filtered index is an optimized nonclustered index that is defined on a subset of rows in a table. A WHERE clause is used to specify which rows are included in the index.
- Reduces index maintenance overhead and storage costs.
- Improves query performance for queries that target the specific subset of rows.
Columnstore Indexes
Columnstore indexes store and process data column by column rather than row by row. They are highly effective for data warehousing workloads and analytical queries involving large amounts of data.
- Achieves high data compression.
- Optimized for batch mode execution, leading to significant performance gains for analytical queries.
Full-Text Indexes
Full-text indexes enable efficient querying of character-based data using linguistic rules. They are used for searching text content within large character columns.
Index Design Considerations
Choosing the Right Columns
Select columns that are frequently used in WHERE clauses, JOIN conditions, ORDER BY, and GROUP BY clauses.
Consider the selectivity of a column: columns with many distinct values are generally better candidates for indexing than columns with few distinct values.
Index Key Order
For multi-column indexes, the order of columns in the index definition is crucial. Place columns with higher selectivity first.
Index Maintenance
Indexes need to be maintained to ensure optimal performance. This includes regular rebuilding or reorganizing of indexes, especially after significant data modifications.
-- Reorganize an index
ALTER INDEX [IndexName] ON [TableName] REORGANIZE;
-- Rebuild an index
ALTER INDEX [IndexName] ON [TableName] REBUILD;
Covering Indexes
A covering index is a nonclustered index that includes all the columns required by a query, either as key columns or as included columns. This allows the query to be satisfied entirely from the index without having to access the base table.
-- Example of a covering index
CREATE NONCLUSTERED INDEX IX_Customers_LastName_FirstName
ON dbo.Customers (LastName, FirstName)
INCLUDE (Email);
Performance Impact
Properly designed indexes can dramatically improve query performance. However, poorly designed or excessive indexes can lead to increased storage costs, slower data modifications (INSERT, UPDATE, DELETE), and increased overhead for the query optimizer.